With the growing popularity of big data analytics in the cloud, a larger emphasis is placed on identifying cloud configurations (e.g., choice of instance types, cluster sizes, etc.) that minimize the associated costs and running times. However, existing approaches for identifying suitable cloud configurations are rarely accurate, require high overhead, and often lack the ability to adapt to different analytical tasks and diverse cloud configurations.